REVIEW
‘Omics’-driven discoveries in prevention and treatment of type 2 diabetes
Anette P. Gjesing,
Oluf Pedersen,
Anette P. Gjesing
The Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health Sciences, University of Copenhagen, Copenhagen, Denmark
Search for more papers by this authorOluf Pedersen
The Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health Sciences, University of Copenhagen, Copenhagen, Denmark
Institute of Biomedical Science, Faculty of Health Sciences, University of Copenhagen, Copenhagen, Denmark
Steno Diabetes Center and Hagedorn Research Institute, Gentofte, Denmark
Faculty of Health Sciences, University of Aarhus, Aarhus, Denmark
Search for more papers by this authorAnette P. Gjesing,
Oluf Pedersen,
Anette P. Gjesing
The Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health Sciences, University of Copenhagen, Copenhagen, Denmark
Search for more papers by this authorOluf Pedersen
The Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health Sciences, University of Copenhagen, Copenhagen, Denmark
Institute of Biomedical Science, Faculty of Health Sciences, University of Copenhagen, Copenhagen, Denmark
Steno Diabetes Center and Hagedorn Research Institute, Gentofte, Denmark
Faculty of Health Sciences, University of Aarhus, Aarhus, Denmark
Search for more papers by this authorOluf Pedersen, The Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health Sciences, University of Copenhagen, Copenhagen, Denmark. Tel.: +45 3533 7121; e-mail: [email protected]
Abstract
Eur J Clin Invest 2012; 42 (6): 579–588
Glucose-based methods are currently gold standards for identifying individuals at risk of type 2 diabetes. Obviously, these methods only consider one of many pathologies of impaired glucose metabolism and they all suffer from a poor specificity as type 2 diabetes risk assessment tools. Recently, however, panels of multiple biomarkers reflecting several pre-diabetic pathologies have been developed. Their specificity and potentials for future risk stratification are discussed. As a multifactorial disorder type 2 diabetes calls for a multifactorial treatment approach targeting multiple but modifiable vascular risk factors. The same holds for pre-diabetic states and prevention hereof. In addition, type 2 diabetes and pre-diabetes show major heterogeneity between affected individuals in pathology, risk of organ damages, progression rate and responsiveness to treatment or prevention. Despite the heterogeneity and uniqueness of type 2 diabetes and pre-diabetes most affected individuals are currently offered interventions as if they all have the same disease or risk of disease and will respond similarly. The complex origin and course of type 2 diabetes combined with uniformity and non-specificity of current interventions may explain the high rate of treatment failures and the relative poor prognosis of many diabetes patients. Given this situation, the present review also explores the perspectives of selected examples within applied genomics and metagenomics for improving patient care by facilitating interventions tailored to specific subpopulations.
References
- 1 Danaei G, Finucane MM, Lu Y, Singh GM, Cowan MJ, Paciorek CJ et al. National, regional, and global trends in fasting plasma glucose and diabetes prevalence since 1980: systematic analysis of health examination surveys and epidemiological studies with 370 country-years and 2.7 million participants. Lancet 2011; 378: 31–40.
- 2 Seshasai SR, Kaptoge S, Thompson A, Di Angelantonio E, Gao P, Sarwar N et al. Diabetes mellitus, fasting glucose, and risk of cause-specific death. N Engl J Med 2011; 364: 829–41.
- 3 Tuomilehto J, Lindstrom J, Eriksson JG, Valle TT, Hamalainen H, Ilanne-Parikka P et al. Prevention of type 2 diabetes mellitus by changes in lifestyle among subjects with impaired glucose tolerance. N Engl J Med 2001; 344: 1343–50.
- 4 Knowler WC, Barrett-Connor E, Fowler SE, Hamman RF, Lachin JM, Walker EA et al. Reduction in the incidence of type 2 diabetes with lifestyle intervention or metformin. N Engl J Med 2002; 346: 393–403.
- 5 Pan XR, Li GW, Hu YH, Wang JX, Yang WY, An ZX et al. Effects of diet and exercise in preventing NIDDM in people with impaired glucose tolerance. The Da Qing IGT and Diabetes Study. Diabetes Care 1997; 20: 537–44.
- 6 Ramachandran A, Snehalatha C, Mary S, Mukesh B, Bhaskar AD, Vijay V. The Indian Diabetes Prevention Programme shows that lifestyle modification and metformin prevent type 2 diabetes in Asian Indian subjects with impaired glucose tolerance (IDPP-1). Diabetologia 2006; 49: 289–97.
- 7 UK Prospective Diabetes Study (UKPDS) Group. Study GUPD Intensive blood-glucose control with sulphonylureas or insulin compared with conventional treatment and risk of complications in patients with type 2 diabetes (UKPDS 33). Lancet 1998; 352: 837–53.
- 8 UK Prospective Diabetes Study (UKPDS) Group. Group U Effect of intensive blood-glucose control with metformin on complications in overweight patients with type 2 diabetes (UKPDS 34). UK Prospective Diabetes Study (UKPDS) Group. Lancet 1998; 352: 854–65.
- 9 Gaede P, Vedel P, Parving HH, Pedersen O. Intensified multifactorial intervention in patients with type 2 diabetes mellitus and microalbuminuria: the Steno type 2 randomised study. Lancet 1999; 353: 617–22.
- 10 Gaede P, Vedel P, Larsen N, Jensen GV, Parving HH, Pedersen O. Multifactorial intervention and cardiovascular disease in patients with type 2 diabetes. N Engl J Med 2003; 348: 383–93.
- 11 Gaede P, Lund-Andersen H, Parving HH, Pedersen O. Effect of a multifactorial intervention on mortality in type 2 diabetes. N Engl J Med 2008; 358: 580–91.
- 12 Gerstein HC, Miller ME, Byington RP, Goff DC Jr, Bigger JT, Buse JB et al. Effects of intensive glucose lowering in type 2 diabetes. N Engl J Med 2008; 358: 2545–59.
- 13 Patel A, MacMahon S, Chalmers J, Neal B, Billot L, Woodward M et al. Intensive blood glucose control and vascular outcomes in patients with type 2 diabetes. N Engl J Med 2008; 358: 2560–72.
- 14 Curb JD, Pressel SL, Cutler JA, Savage PJ, Applegate WB, Black H et al. Effect of diuretic-based antihypertensive treatment on cardiovascular disease risk in older diabetic patients with isolated systolic hypertension. Systolic Hypertension in the Elderly Program Cooperative Research Group. JAMA 1996; 276: 1886–92.
- 15 Staessen JA, Fagard R, Thijs L, Celis H, Arabidze GG, Birkenhager WH et al. Randomised double-blind comparison of placebo and active treatment for older patients with isolated systolic hypertension. The Systolic Hypertension in Europe (Syst-Eur) Trial Investigators. Lancet 1997; 350: 757–64.
- 16 UKPDS. UPDSG Tight blood pressure control and risk of macrovascular and microvascular complications in type 2 diabetes: UKPDS 38. BMJ 1998; 317: 703–13.
- 17 Heart Outcomes Prevention Evaluation (HOPE) Study Investigators. Effects of ramipril on cardiovascular and microvascular outcomes in people with diabetes mellitus: results of the HOPE study and MICRO-HOPE substudy. Heart Outcomes Prevention Evaluation Study Investigators. Lancet 2000; 355: 253–9.
- 18 Pyorala K, Pedersen TR, Kjekshus J, Faergeman O, Olsson AG, Thorgeirsson G. Cholesterol lowering with simvastatin improves prognosis of diabetic patients with coronary heart disease. A subgroup analysis of the Scandinavian Simvastatin Survival Study (4S). Diabetes Care 1997; 20: 614–20.
- 19 Holman RR, Paul SK, Bethel MA, Matthews DR, Neil HA. 10-year follow-up of intensive glucose control in type 2 diabetes. N Engl J Med 2008; 359: 1577–89.
- 20 Collins R, Armitage J, Parish S, Sleigh P, Peto R. MRC/BHF Heart Protection Study of cholesterol-lowering with simvastatin in 5963 people with diabetes: a randomised placebo-controlled trial. Lancet 2003; 361: 2005–16.
- 21 Ginsberg HN, Elam MB, Lovato LC, Crouse JR 3rd, Leiter LA, Linz P et al. Effects of combination lipid therapy in type 2 diabetes mellitus. N Engl J Med 2010; 362: 1563–74.
- 22 Dale AC, Vatten LJ, Nilsen TI, Midthjell K, Wiseth R. Secular decline in mortality from coronary heart disease in adults with diabetes mellitus: cohort study. BMJ 2008; 337: a236.
- 23 Gulliford MC, Charlton J. Is relative mortality of type 2 diabetes mellitus decreasing? Am J Epidemiol 2009; 169: 455–61.
- 24 American Diabetes Association. Standards of medical care in diabetes – 2011. Diabetes Care 2011; 34(Suppl 1): S11–61.
- 25 Handelsman Y, Jellinger PS. Overcoming obstacles in risk factor management in type 2 diabetes mellitus. J Clin Hypertens (Greenwich) 2011; 13: 613–20.
- 26 Stern MP, Williams K, Haffner SM. Identification of persons at high risk for type 2 diabetes mellitus: do we need the oral glucose tolerance test? Ann Intern Med 2002; 136: 575–81.
- 27 Abdul-Ghani MA, Williams K, DeFronzo RA, Stern M. What is the best predictor of future type 2 diabetes? Diabetes Care 2007; 30: 1544–8.
- 28 Wilson PW, Meigs JB, Sullivan L, Fox CS, Nathan DM, D’Agostino RB. Sr. Prediction of incident diabetes mellitus in middle-aged adults: the Framingham Offspring Study. Arch Intern Med 2007; 167: 1068–74.
- 29 Kolberg JA, Jorgensen T, Gerwien RW, Hamren S, McKenna MP, Moler E et al. Development of a type 2 diabetes risk model from a panel of serum biomarkers from the Inter99 cohort. Diabetes Care 2009; 32: 1207–12.
- 30 Urdea M, Kolberg J, Wilber J, Gerwien R, Moler E, Rowe M et al. Validation of a multimarker model for assessing risk of type 2 diabetes from a five-year prospective study of 6784 Danish people (Inter99). J Diabetes Sci Technol 2009; 3: 748–55.
- 31 Shafizadeh TB, Moler EJ, Kolberg JA, Nguyen UT, Hansen T, Jorgensen T et al. Comparison of accuracy of diabetes risk score and components of the metabolic syndrome in assessing risk of incident type 2 diabetes in inter99 cohort. PLoS ONE 2011; 6: e22863.
- 32 Lyssenko V, Jorgensen T, Gerwien RW, Hansen T, Rowe MW, McKenna MP et al. Validation of a multi-marker model for the prediction of incident type 2 diabetes mellitus: combined results of the Inter99 and Botnia studies. Diab Vasc Dis Res 2012; 9: 59–67.
- 33 Wang TJ, Larson MG, Vasan RS, Cheng S, Rhee EP, McCabe E et al. Metabolite profiles and the risk of developing diabetes. Nat Med 2011; 17: 448–53.
- 34 Jakobsen LH, Kondrup J, Zellner M, Tetens I, Roth E. Effect of a high protein meat diet on muscle and cognitive functions: a randomised controlled dietary intervention trial in healthy men. Clin Nutr 2011; 30: 303–11.
- 35 Murphy R, Ellard S, Hattersley AT. Clinical implications of a molecular genetic classification of monogenic beta-cell diabetes. Nat Clin Pract Endocrinol Metab 2008; 4: 200–13.
- 36 McCarthy MI. Genomics, type 2 diabetes, and obesity. N Engl J Med 2010; 363: 2339–50.
- 37 Grarup N, Sparso T, Hansen T. Physiologic characterization of type 2 diabetes-related loci. Curr Diab Rep 2010; 10: 485–97.
- 38 So HC, Gui AH, Cherny SS, Sham PC. Evaluating the heritability explained by known susceptibility variants: a survey of ten complex diseases. Genet Epidemiol 2011; 35: 310–7.
- 39 Durbin RM, Abecasis GR, Altshuler DL, Auton A, Brooks LD, Gibbs RA et al. A map of human genome variation from population-scale sequencing. Nature 2010; 467: 1061–73.
- 40 Sebat J, Lakshmi B, Troge J, Alexander J, Young J, Lundin P et al. Large-scale copy number polymorphism in the human genome. Science 2004; 305: 525–8.
- 41 Henrichsen CN, Vinckenbosch N, Zollner S, Chaignat E, Pradervand S, Schutz F et al. Segmental copy number variation shapes tissue transcriptomes. Nat Genet 2009; 41: 424–9.
- 42 Bell JT, Timpson NJ, Rayner NW, Zeggini E, Frayling TM, Hattersley AT et al. Genome-wide association scan allowing for epistasis in type 2 diabetes. Ann Hum Genet 2011; 75: 10–9.
- 43 Nettleton JA, McKeown NM, Kanoni S, Lemaitre RN, Hivert MF, Ngwa J et al. Interactions of dietary whole-grain intake with fasting glucose- and insulin-related genetic loci in individuals of European descent: a meta-analysis of 14 cohort studies. Diabetes Care 2010; 33: 2684–91.
- 44 Van Steen K. Travelling the world of gene-gene interactions. Brief Bioinform 2012; 13: 1–19.
- 45 Barres R, Zierath JR. DNA methylation in metabolic disorders. Am J Clin Nutr 2011; 93: 897S–900.
- 46 Groop L, Lyssenko V. Genetic basis of beta-cell dysfunction in man. Diabetes Obes Metab 2009; 11(Suppl 4): 149–58.
- 47 Zaina S, Perez-Luque EL, Lund G. Genetics talks to epigenetics? The interplay between sequence variants and chromatin structure Curr Genomics 2010; 11: 359–67.
- 48 Pearson ER, Donnelly LA, Kimber C, Whitley A, Doney AS, McCarthy MI et al. Variation in TCF7L2 influences therapeutic response to sulfonylureas: a GoDARTs study. Diabetes 2007; 56: 2178–82.
- 49 Zhou K, Bellenguez C, Spencer CC, Bennett AJ, Coleman RL, Tavendale R et al. Common variants near ATM are associated with glycemic response to metformin in type 2 diabetes. Nat Genet 2011; 43: 117–20.
- 50 Handelsman J. Metagenomics: application of genomics to uncultured microorganisms. Microbiol Mol Biol Rev 2004; 68: 669–85.
- 51 Spor A, Koren O, Ley R. Unravelling the effects of the environment and host genotype on the gut microbiome. Nat Rev Microbiol 2011; 9: 279–90.
- 52 Diamant M, Blaak EE, de Vos WM. Do nutrient-gut-microbiota interactions play a role in human obesity, insulin resistance and type 2 diabetes? Obes Rev 2011; 12: 272–81.
- 53 Qin J, Li R, Raes J, Arumugam M, Burgdorf KS, Manichanh C et al. A human gut microbial gene catalogue established by metagenomic sequencing. Nature 2010; 464: 59–65.
- 54 Arumugam M, Raes J, Pelletier E, Le Paslier D, Yamada T, Mende DR et al. Enterotypes of the human gut microbiome. Nature 2011; 473: 174–80.
- 55 Dethlefsen L, Huse S, Sogin ML, Relman DA. The pervasive effects of an antibiotic on the human gut microbiota, as revealed by deep 16S rRNA sequencing. PLoS Biol 2008; 6: e280.
- 56 Musso G, Gambino R, Cassader M. Obesity, diabetes, and gut microbiota: the hygiene hypothesis expanded? Diabetes Care 2010; 33: 2277–84.
- 57 Delzenne NM, Neyrinck AM, Backhed F, Cani PD. Targeting gut microbiota in obesity: effects of prebiotics and probiotics. Nat Rev Endocrinol 2011; 7(11): 639–46.
- 58 Vrieze A, Holleman F, Zoetendal EG, de Vos WM, Hoekstra JB, Nieuwdorp M. The environment within: how gut microbiota may influence metabolism and body composition. Diabetologia 2010; 53: 606–13.
- 59 Turnbaugh PJ, Hamady M, Yatsunenko T, Cantarel BL, Duncan A, Ley RE et al. A core gut microbiome in obese and lean twins. Nature 2009; 457: 480–4.
- 60 Vijay-Kumar M, Aitken JD, Carvalho FA, Cullender TC, Mwangi S, Srinivasan S et al. Metabolic syndrome and altered gut microbiota in mice lacking Toll-like receptor 5. Science 2010; 328: 228–31.
- 61 Larsen N, Vogensen FK, van den Berg FW, Nielsen DS, Andreasen AS, Pedersen BK et al. Gut microbiota in human adults with type 2 diabetes differs from non-diabetic adults. PLoS ONE 2010; 5: e9085.
- 62 Ley RE, Backhed F, Turnbaugh P, Lozupone CA, Knight RD, Gordon JI. Obesity alters gut microbial ecology. Proc Natl Acad Sci USA 2005; 102: 11070–5.
- 63 Turnbaugh PJ, Ley RE, Mahowald MA, Magrini V, Mardis ER, Gordon JI. An obesity-associated gut microbiome with increased capacity for energy harvest. Nature 2006; 444: 1027–31.
- 64 Backhed F, Manchester JK, Semenkovich CF, Gordon JI. Mechanisms underlying the resistance to diet-induced obesity in germ-free mice. Proc Natl Acad Sci U S A 2007; 104: 979–84.
- 65 Hildebrandt MA, Hoffmann C, Sherrill-Mix SA, Keilbaugh SA, Hamady M, Chen YY et al. High-fat diet determines the composition of the murine gut microbiome independently of obesity. Gastroenterology 2009; 137: 1716–24.
- 66 Delzenne NM, Cani PD. Gut microbiota and the pathogenesis of insulin resistance. Curr Diab Rep 2011; 11: 154–9.
- 67 Cani PD, Delzenne NM. The role of the gut microbiota in energy metabolism and metabolic disease. Curr Pharm Des 2009; 15: 1546–58.
- 68 Cani PD, Neyrinck AM, Fava F, Knauf C, Burcelin RG, Tuohy KM et al. Selective increases of bifidobacteria in gut microflora improve high-fat-diet-induced diabetes in mice through a mechanism associated with endotoxaemia. Diabetologia 2007; 50: 2374–83.
- 69 Jumpertz R, Le DS, Turnbaugh PJ, Trinidad C, Bogardus C, Gordon JI et al. Energy-balance studies reveal associations between gut microbes, caloric load, and nutrient absorption in humans. Am J Clin Nutr 2011; 94: 58–65.
